This is the R file including all the content related to the simulated data based on real data analysis. Please notice that this is a simulation, so the execution time would be around 4 hours. The main content of this file includes:
Please notice that since this is the simulation based on real data, we would use the cleaned data and clustering result of the real data analysis, and you would found that we have saved those results in the following files and read in this code:
PLEASE USING WINDOWS SYSTEM to execute the code so as to reproduce the figures and tables in the paper! Ubuntu and OS system have different setting on seed setting, so the execution result would be slightly different on these two system. Moreover, our key algorithm FunFEM would diverge or fail on some seed, so we strongly suggest to use WINDOWS system to execute the code.
In addition, FunFEM would try multiple variance and covariance matrix form to get the best result. In some forms, the result would be diverge and you may see some warning or message like
"Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% : infinite or missing values in ‘x’" “Error in svd(X) : infinite or missing values in ‘x’”
This is not a real error and the code would keep running.
# ipak, a function for checking the the installation of the packages
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE)
sapply(pkg, require, character.only = TRUE)
}
# add new packages here
packages <- c("ggplot2", "dplyr", "tidyverse", "tidyr", "ggridges",
"scales","geofacet","lubridate","fda","fda.usc","RColorBrewer",
"ggrepel","DT","ggthemes","fpc","clue",
"gridExtra","factoextra", "NbClust","funFEM","mapproj")
ipak(packages)
## ggplot2 dplyr tidyverse tidyr ggridges scales
## TRUE TRUE TRUE TRUE TRUE TRUE
## geofacet lubridate fda fda.usc RColorBrewer ggrepel
## TRUE TRUE TRUE TRUE TRUE TRUE
## DT ggthemes fpc clue gridExtra factoextra
## TRUE TRUE TRUE TRUE TRUE TRUE
## NbClust funFEM mapproj
## TRUE TRUE TRUE
##Loading the unsmoothed non-missing data and cluster information
mindiff.date.1 = read.csv("mindiff_day1_nona.csv")
mindiff.date.1$date = as.Date(mindiff.date.1$x,format = "%Y-%m-%d")
mindiff.state.1 = read.csv("mindiff_state1_nona.csv")
cluster.result = read.csv("real_analysis_result.csv")
## Generate curves based on real data
generate.curve.real<-function(n.group, seed, plotting = F){
## Simulating 4 clusters based on real application result
# IN cluster 1
clu.1 = cluster.result[cluster.result$fem.4 == 1,]$state
clu.1.raw = mindiff.state.1[,names(mindiff.state.1)%in%clu.1]
clu.1.rawmat.1 = matrix(unlist(clu.1.raw),ncol = ncol(clu.1.raw))
bks.1 = as.Date(quantile(unclass(as.Date(mindiff.date.1$date)), seq(0,1,length = 15)), origin = "1970-01-01")
err1.basis = create.bspline.basis(rangeval = c(min(mindiff.date.1$date),max(mindiff.date.1$date)), breaks = bks.1, norder = 4)
clu.1.fd<-smooth.basis(argvals = mindiff.date.1$date, y = clu.1.rawmat.1, err1.basis)$fd
clu.1.fpca = pca.fd(clu.1.fd,nharm=3)
clu.1.fpca.loading = clu.1.fpca$harmonics$coefs
clu.1.fpca.lambda = clu.1.fpca$values[1:3]
clu.1.mean.coef = clu.1.fpca$meanfd$coefs
## Simulate n curves:
set.seed(seed)
clu.1.score1 = rnorm(n.group,0,sqrt(clu.1.fpca.lambda[1]))
clu.1.score2 = rnorm(n.group,0,sqrt(clu.1.fpca.lambda[2]))
clu.1.score3 = rnorm(n.group,0,sqrt(clu.1.fpca.lambda[3]))
clu.1.scores = rbind(clu.1.score1,clu.1.score2,clu.1.score3)
clu.1.simcoeff = t(clu.1.scores)%*%t(clu.1.fpca.loading)+matrix(clu.1.mean.coef,nrow=n.group,ncol=length(clu.1.mean.coef),byrow=TRUE)
clu.1.sim.df = clu.1.fd
clu.1.sim.df$coefs = t(clu.1.simcoeff)
clu.1.sim.data = predict(clu.1.sim.df,mindiff.date.1$date)
name.clu1 = paste0("clu1.",1:n.group)
colnames(clu.1.sim.data) <- name.clu1
clu.1.sim.data = data.frame(clu.1.sim.data)
# IN cluster 2
clu.2 = cluster.result[cluster.result$fem.4 == 2,]$state
clu.2.raw = mindiff.state.1[,names(mindiff.state.1)%in%clu.2]
clu.2.rawmat.1 = matrix(unlist(clu.2.raw),ncol = ncol(clu.2.raw))
clu.2.fd<-smooth.basis(argvals = mindiff.date.1$date, y = clu.2.rawmat.1, err1.basis)$fd
clu.2.fpca = pca.fd(clu.2.fd,nharm=3)
clu.2.fpca.loading = clu.2.fpca$harmonics$coefs
clu.2.fpca.lambda = clu.2.fpca$values[1:3]
clu.2.mean.coef = clu.2.fpca$meanfd$coefs
## Simulate n curves:
set.seed(seed)
clu.2.score1 = rnorm(n.group,0,sqrt(clu.2.fpca.lambda[1]))
clu.2.score2 = rnorm(n.group,0,sqrt(clu.2.fpca.lambda[2]))
clu.2.score3 = rnorm(n.group,0,sqrt(clu.2.fpca.lambda[3]))
clu.2.scores = rbind(clu.2.score1,clu.2.score2,clu.2.score3)
clu.2.simcoeff = t(clu.2.scores)%*%t(clu.2.fpca.loading)+matrix(clu.2.mean.coef,nrow=n.group,ncol=length(clu.2.mean.coef),byrow=TRUE)
clu.2.sim.df = clu.2.fd
clu.2.sim.df$coefs = t(clu.2.simcoeff)
clu.2.sim.data = predict(clu.2.sim.df,mindiff.date.1$date)
name.clu2 = paste0("clu2.",1:n.group)
colnames(clu.2.sim.data) <- name.clu2
clu.2.sim.data = data.frame(clu.2.sim.data)
# IN cluster 3
clu.3 = cluster.result[cluster.result$fem.4 == 3,]$state
clu.3.raw = mindiff.state.1[,names(mindiff.state.1)%in%clu.3]
clu.3.rawmat.1 = matrix(unlist(clu.3.raw),ncol = ncol(clu.3.raw))
clu.3.fd<-smooth.basis(argvals = mindiff.date.1$date, y = clu.3.rawmat.1, err1.basis)$fd
clu.3.fpca = pca.fd(clu.3.fd,nharm=3)
clu.3.fpca.loading = clu.3.fpca$harmonics$coefs
clu.3.fpca.lambda = clu.3.fpca$values[1:3]
clu.3.mean.coef = clu.3.fpca$meanfd$coefs
## Simulate n curves:
set.seed(seed)
clu.3.score1 = rnorm(n.group,0,sqrt(clu.3.fpca.lambda[1]))
clu.3.score2 = rnorm(n.group,0,sqrt(clu.3.fpca.lambda[2]))
clu.3.score3 = rnorm(n.group,0,sqrt(clu.3.fpca.lambda[3]))
clu.3.scores = rbind(clu.3.score1,clu.3.score2,clu.3.score3)
clu.3.simcoeff = t(clu.3.scores)%*%t(clu.3.fpca.loading)+matrix(clu.3.mean.coef,nrow=n.group,ncol=length(clu.3.mean.coef),byrow=TRUE)
clu.3.sim.df = clu.3.fd
clu.3.sim.df$coefs = t(clu.3.simcoeff)
clu.3.sim.data = predict(clu.3.sim.df,mindiff.date.1$date)
name.clu3 = paste0("clu3.",1:n.group)
colnames(clu.3.sim.data) <- name.clu3
clu.3.sim.data = data.frame(clu.3.sim.data)
# IN cluster 4
clu.4 = cluster.result[cluster.result$fem.4 == 4,]$state
clu.4.raw = mindiff.state.1[,names(mindiff.state.1)%in%clu.4]
clu.4.rawmat.1 = matrix(unlist(clu.4.raw),ncol = ncol(clu.4.raw))
clu.4.fd<-smooth.basis(argvals = mindiff.date.1$date, y = clu.4.rawmat.1, err1.basis)$fd
clu.4.fpca = pca.fd(clu.4.fd,nharm=3)
clu.4.fpca.loading = clu.4.fpca$harmonics$coefs
clu.4.fpca.lambda = clu.4.fpca$values[1:3]
clu.4.mean.coef = clu.4.fpca$meanfd$coefs
## Simulate n curves:
set.seed(seed)
clu.4.score1 = rnorm(n.group,0,sqrt(clu.4.fpca.lambda[1]))
clu.4.score2 = rnorm(n.group,0,sqrt(clu.4.fpca.lambda[2]))
clu.4.score3 = rnorm(n.group,0,sqrt(clu.4.fpca.lambda[3]))
clu.4.scores = rbind(clu.4.score1,clu.4.score2,clu.4.score3)
clu.4.simcoeff = t(clu.4.scores)%*%t(clu.4.fpca.loading)+matrix(clu.4.mean.coef,nrow=n.group,ncol=length(clu.4.mean.coef),byrow=TRUE)
clu.4.sim.df = clu.4.fd
clu.4.sim.df$coefs = t(clu.4.simcoeff)
clu.4.sim.data = predict(clu.4.sim.df,mindiff.date.1$date)
name.clu4 = paste0("clu4.",1:n.group)
colnames(clu.4.sim.data) <- name.clu4
clu.4.sim.data = data.frame(clu.4.sim.data)
simu.all = cbind(clu.1.sim.data,clu.2.sim.data,clu.3.sim.data,clu.4.sim.data)
simu.all$t = mindiff.date.1$date
if(plotting == T){
par(mfrow=c(2,2))
plot(clu.1.sim.df,ylim = c(0,10))
plot(clu.2.sim.df,ylim = c(0,10))
plot(clu.3.sim.df,ylim = c(0,10))
plot(clu.4.sim.df,ylim = c(0,10))
}
return (simu.all)
}
## Functions for Cluster Number Selection Study
cluster.select.num.real = function(simu.all,n.group,seed,plotting = FALSE){
t = simu.all$t
simu.all = simu.all[,-which(names(simu.all) %in% c("t"))]
simu.all.mat = matrix(unlist(simu.all),ncol = 4*n.group)
bks.1 = as.Date(quantile(unclass(as.Date(mindiff.date.1$date)), seq(0,1,length = 12)), origin = "1970-01-01")
err1.basis = create.bspline.basis(rangeval = c(min(mindiff.date.1$date),max(mindiff.date.1$date)), breaks = bks.1, norder = 4)
simu.fd<-smooth.basis(argvals = t, y = simu.all.mat, err1.basis)$fd
if(plotting == TRUE){plot(simu.fd)}
## FunFEM
set.seed(seed)
fem.clu.bic = funFEM(simu.fd,K = 2:6,model = "all",crit = "bic", init = "kmeans",eps = 1e-5)
fem.num.bic = fem.clu.bic$K
fem.clu.icl = funFEM(simu.fd,K = 2:6,model = "all",crit = "icl", init = "kmeans",eps = 1e-5)
fem.num.icl = fem.clu.icl$K
set.seed(seed)
## kmeans on smoothed FPC scores
range = c(as.numeric(min(t)),as.numeric(max(t)))
bspFpcPen = fdPar(fdobj=err1.basis, Lfdobj=2, lambda = 2*10^6)
smooth.fpc = pca.fd(simu.fd,nharm=3,harmfdPar=bspFpcPen)
fd.scores = smooth.fpc$scores
# all index
set.seed(seed)
fpc.kmeans.best <- NbClust(fd.scores, distance = "euclidean",
min.nc=2, max.nc=6,
method = "kmeans",
index = "all")
fpc.clu.select = max(fpc.kmeans.best$Best.partition)
# ## kmeans on coef -- would experience too many error, temporarly skip
# fd.coefs = t(simu.fd$coefs)
# # all index
# coeff.kmeans.best <- NbClust(fd.coefs, distance = "euclidean",
# min.nc=2, max.nc=6,
# method = "centroid", # due to the robust
# index = "all")
# bsp.clu.select = max(coeff.kmeans.best$Best.partition)
simu.clunum.compare = rbind(fem.num.bic,fem.num.icl,
fpc.clu.select)
# bsp.clu.select: Easy to experience error, skip
return(simu.clunum.compare)
}
# Simulation plot sample for Fig 9
set.seed(888)
generate.curve.real(20,seed = 888,plotting = TRUE)
##Clustering Number Selection Validation Simulation
simunum.real.20= data.frame(matrix(ncol = 4, nrow = 1))
colnames(simunum.real.20) <- c("method","index","clu.num","n.group")
seed = 888
for (i in 1:200){
set.seed(seed)
df = generate.curve.real(20,seed = seed)
clunum.result = data.frame(matrix(ncol = 4, nrow = 3))
colnames(clunum.result) <- c("method","index","clu.num","n.group")
# clunum.result$method = c("fem","fem","bsp","fpc")
clunum.result$method = c("fem","fem","fpc")
# clunum.result$index = c("bic","icl","all","all")
clunum.result$index = c("bic","icl","all")
clunum.result$clu.num = cluster.select.num.real(simu.all=df, seed = seed,n.group=20)
clunum.result$n.group = rep(20,each = 3)
simunum.real.20 = rbind(simunum.real.20,clunum.result)
seed = seed+10000*i
}
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 5 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 8 proposed 2 as the best number of clusters
## * 6 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 4 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 2
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 5 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 15 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 3 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 7 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 2
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 6 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 3 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 12 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 5 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 11 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 7 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 5 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 3 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 7 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 2
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 15 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 5 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 5 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 3 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 5 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 5 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 11 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 2 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 7 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 4 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 3 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 5 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 6 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 5 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 7 proposed 2 as the best number of clusters
## * 6 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 17 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 5 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 6 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 3 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 7 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 7 proposed 2 as the best number of clusters
## * 6 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 3 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 6 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 15 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 5 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 8 proposed 2 as the best number of clusters
## * 6 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 2 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 8 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 6 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 15 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 12 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 4 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 2 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 6 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 6 proposed 5 as the best number of clusters
## * 5 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 9 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 2 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 9 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 6 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 3 proposed 2 as the best number of clusters
## * 14 proposed 3 as the best number of clusters
## * 2 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 3 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 6 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 3 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 2 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 6 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 4 proposed 5 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 6 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 6 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 16 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 15 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 16 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 12 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 10 proposed 3 as the best number of clusters
## * 7 proposed 4 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 1 proposed 2 as the best number of clusters
## * 13 proposed 3 as the best number of clusters
## * 5 proposed 4 as the best number of clusters
## * 2 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 9 proposed 3 as the best number of clusters
## * 8 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 6 proposed 3 as the best number of clusters
## * 10 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 4 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 7 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 8 proposed 2 as the best number of clusters
## * 11 proposed 3 as the best number of clusters
## * 4 proposed 4 as the best number of clusters
## * 1 proposed 6 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************
simunum.real.20 = na.omit(simunum.real.20)
write.csv(simunum.real.20, file = "simunum_real_20.csv", row.names = FALSE)
### Table 4: FEM (BIC and ICL part) and Kmeans on FPC scores part
## Summary analysis of the cluster number detection.
clunum.sim.20 = read.csv("simunum_real_20.csv",header = TRUE)
clunum.sim.20.summary = clunum.sim.20%>%group_by(n.group, method,
index, clu.num)%>%summarize(count = n())
clunum.sim.20.summary
## Cluster Number Detection Simulation: Kmeans on B-spline Coefficients
simunum.bspcoeff.20= data.frame(matrix(ncol = 4, nrow = 1))
colnames(simunum.bspcoeff.20) <- c("method","index","clu.num","n.group")
seed = 888
for (i in 1:200){
set.seed(seed)
df = generate.curve.real(20,seed = seed)
t = df$t
df = df[,-which(names(df) %in% c("t"))]
df.mat = matrix(unlist(df),ncol = 4*20)
bks.1 = as.Date(quantile(unclass(as.Date(mindiff.date.1$date)), seq(0,1,length = 12)), origin = "1970-01-01")
err1.basis = create.bspline.basis(rangeval = c(min(mindiff.date.1$date),max(mindiff.date.1$date)), breaks = bks.1, norder = 4)
simu.fd<-smooth.basis(argvals = t, y = df.mat, err1.basis)$fd
fd.coefs = t(simu.fd$coefs)
clunum.result.bspcoeff = data.frame(matrix(ncol = 4, nrow = 1))
colnames(clunum.result.bspcoeff) <- c("method","index","clu.num","n.group")
clunum.result.bspcoeff$method = "kmeans on B-spline Coeff"
clunum.result.bspcoeff$index = "average silhouette width"
set.seed(seed)
pamk.best <- pamk(fd.coefs,krange=2:6)
clunum.result.bspcoeff$clu.num = pamk.best$nc
clunum.result.bspcoeff$n.group = 20
simunum.bspcoeff.20 = rbind(simunum.bspcoeff.20,clunum.result.bspcoeff)
seed = seed+10000*i
}
simunum.bspcoeff.20 = na.omit(simunum.bspcoeff.20)
write.csv(simunum.bspcoeff.20, file = "simunum_bspcoeff_20.csv", row.names = FALSE)
### Table 4: Kmeans on Coefficients part
## Summary analysis of the cluster number detection.
clunum.simunum.bspcoeff.20 = read.csv("simunum_bspcoeff_20.csv",header = TRUE)
clunum.simunum.bspcoeff.20.summary = clunum.simunum.bspcoeff.20%>%group_by(n.group, method,
index, clu.num)%>%summarize(count = n())
clunum.simunum.bspcoeff.20.summary
##Clustering Validation Study Functions
# Hungarian Algorithm
minWeightBipartiteMatching <- function(clusteringA, clusteringB) {
require(clue)
idsA <- unique(clusteringA) # distinct cluster ids in a
idsB <- unique(clusteringB) # distinct cluster ids in b
nA <- length(clusteringA) # number of instances in a
nB <- length(clusteringB) # number of instances in b
if (length(idsA) != length(idsB) || nA != nB) {
stop("number of cluster or number of instances do not match")
}
nC <- length(idsA)
tupel <- c(1:nA)
# computing the distance matrix
assignmentMatrix <- matrix(rep(-1, nC * nC), nrow = nC)
for (i in 1:nC) {
tupelClusterI <- tupel[clusteringA == i]
solRowI <- sapply(1:nC, function(i, clusterIDsB, tupelA_I) {
nA_I <- length(tupelA_I) # number of elements in cluster I
tupelB_I <- tupel[clusterIDsB == i]
nB_I <- length(tupelB_I)
nTupelIntersect <- length(intersect(tupelA_I, tupelB_I))
return((nA_I - nTupelIntersect) + (nB_I - nTupelIntersect))
}, clusteringB, tupelClusterI)
assignmentMatrix[i, ] <- solRowI
}
# optimization
result <- solve_LSAP(assignmentMatrix, maximum = FALSE)
attr(result, "assignmentMatrix") <- assignmentMatrix
return(result)
}
# Function to get cluster result
get.df.cluster = function(simu.all,n.group,seed){
t = simu.all$t
simu.all = simu.all[,-which(names(simu.all) %in% c("t"))]
simu.all.mat = matrix(unlist(simu.all),ncol = 4*n.group)
bks.1 = as.Date(quantile(unclass(as.Date(mindiff.date.1$date)), seq(0,1,length = 12)), origin = "1970-01-01")
simu.basis = create.bspline.basis(rangeval = c(min(mindiff.date.1$date),max(mindiff.date.1$date)), breaks = bks.1, norder = 4)
simu.fd<-smooth.basis(argvals = t, y = simu.all.mat, simu.basis)$fd
# fem on icl
set.seed(seed)
fem.icl <- funFEM(simu.fd,K = 4,model = "all",crit = "icl", init = "kmeans",eps = 1e-5) # 4 cluster solution
df.fem.icl <- fem.icl$cls
# fem on bic
set.seed(seed)
fem.bic <- funFEM(simu.fd,K = 4,model = "all",crit = "bic", init = "kmeans",eps = 1e-5) # 4 cluster solution
df.fem.bic <- fem.bic$cls
# kmeans on bsp coeff
set.seed(seed)
simu.fd.coef = t(simu.fd$coefs)
coeff.k <- kmeans(simu.fd.coef, 4) # 4 cluster solution
df.bsp.k <- coeff.k$cluster
# Kmeans on smoothed pc scores
range = c(as.numeric(min(t)),as.numeric(max(t)))
bspFpcPen = fdPar(fdobj=simu.basis, Lfdobj=2, lambda = 2e6)
## We will explain this lambda selection in the next part
smooth.fpc = pca.fd(simu.fd,nharm=3,harmfdPar=bspFpcPen)
df.pcscore = smooth.fpc$scores
set.seed(seed)
fpc.k <- kmeans(df.pcscore, 4) # 4 cluster solution
df.fpc.k <- fpc.k$cluster
# Hungarian Alg
real = rep(c(1,2,3,4),each = n.group)
a = minWeightBipartiteMatching(df.bsp.k,real)
df.bsp.k.1 = a[df.bsp.k]
b = minWeightBipartiteMatching(df.fpc.k,real)
df.fpc.k.1 = b[df.fpc.k]
c = minWeightBipartiteMatching(df.fem.bic,real)
df.fem.bic.1 = c[df.fem.bic]
d = minWeightBipartiteMatching(df.fem.icl,real)
df.fem.icl.1 = d[df.fem.icl]
clu.result = data.frame(clu.real = real,
clu.bsp = df.bsp.k.1,
clu.fpc = df.fpc.k.1,
clu.fem.bic = df.fem.bic.1,
clu.fem.icl = df.fem.icl.1)
return(clu.result)
}
# Accuracy test
get.clu.accuracy = function(df.clu, n.group){
bsp.accu = sum(df.clu$clu.real == df.clu$clu.bsp)/(n.group*4)
fpc.accu = sum(df.clu$clu.real == df.clu$clu.fpc)/(n.group*4)
fem.bic.accu = sum(df.clu$clu.real == df.clu$clu.fem.bic)/(n.group*4)
fem.icl.accu = sum(df.clu$clu.real == df.clu$clu.fem.icl)/(n.group*4)
return(c(bsp.accu,fpc.accu,fem.bic.accu,fem.icl.accu))
}
##Clustering Number Selection Validation Simulation
simuvid.real.20= data.frame(matrix(ncol = 4, nrow = 1))
colnames(simuvid.real.20) <- c("method","index","n.group","accuracy")
seed = 888
for (i in 1:200){
set.seed(seed)
df = generate.curve.real(20,seed = seed)
cluvid.result = data.frame(matrix(ncol = 4, nrow = 4))
colnames(cluvid.result) <- c("method","index","n.group","accuracy")
cluvid.result$method = c("bsp","fpc","fem","fem")
cluvid.result$index = c("all","all","bic","icl")
cluvid.result$n.group = rep(20,each = 4)
df.clu = get.df.cluster(df,n.group = 20,seed = seed)
cluvid.result$accuracy = get.clu.accuracy(df.clu, 20)
simuvid.real.20 = rbind(simuvid.real.20,cluvid.result)
seed = seed+10000*i
}
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(X) : infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
## Error in svd(ginv(t(G) %*% G %*% W) %*% (t(G) %*% Ttilde %*% t(Ttilde) %*% :
## infinite or missing values in 'x'
simuvid.real.20 = na.omit(simuvid.real.20)
write.csv(simuvid.real.20, file = "simuvid_real_20.csv", row.names = FALSE)
### Table 5: Summary the validation result
## This contains the Kmeans on Coeffs and FPC scores, and FEM BIC and ICL
simuvid.raw = read.csv("simuvid_real_20.csv",header = TRUE)
simuvid.raw.summary = simuvid.raw%>%
group_by(n.group, method,index)%>%
summarize(mean.accuracy = mean(accuracy),
sd.accuracy = sd(accuracy),
se.accuracy = sd(accuracy)/sqrt(200))
simuvid.raw.summary
## Kmeans on Raw Data
## Select Number of Cluster
simunum.realraw.20= data.frame(matrix(ncol = 4, nrow = 1))
colnames(simunum.realraw.20) <- c("method","index","clu.num","n.group")
seed = 888
for (i in 1:200){
set.seed(seed)
df = generate.curve.real(20,seed = seed)
df = df[,-which(names(df) %in% c("t"))]
clunum.result.raw = data.frame(matrix(ncol = 4, nrow = 1))
colnames(clunum.result.raw) <- c("method","index","clu.num","n.group")
clunum.result.raw$method = "kmeans on raw"
clunum.result.raw$index = "average silhouette width"
set.seed(seed)
pamk.best <- pamk(df,krange=2:6)
clunum.result.raw$clu.num = pamk.best$nc
clunum.result.raw$n.group = 20
simunum.realraw.20 = rbind(simunum.realraw.20,clunum.result.raw)
seed = seed+10000*i
}
simunum.realraw.20 = na.omit(simunum.realraw.20)
write.csv(simunum.realraw.20, file = "simunum_realraw_20.csv", row.names = FALSE)
### Summary analysis of the cluster number detection.
clunum.sim.20.raw = read.csv("simunum_realraw_20.csv",header = TRUE)
clunum.sim.20.raw.summary = clunum.sim.20.raw%>%group_by(n.group, method,
index, clu.num)%>%summarize(count = n())
clunum.sim.20.raw.summary
## Function for Cluster Result Validation
get.clu.accuracy.raw = function(df.clu, n.group){
# Hungarian Alg
real = rep(c(1,2,3,4),each = n.group)
a = minWeightBipartiteMatching(df.clu, real)
df.clu.1 = a[df.clu]
raw.accu = sum(real == df.clu.1)/(n.group*4)
return(raw.accu)
}
#### Start the Simulation
simuvid.realraw.20= data.frame(matrix(ncol = 4, nrow = 1))
colnames(simuvid.realraw.20) <- c("method","index","n.group","accuracy")
seed = 88
for (i in 1:200){
set.seed(seed)
df = generate.curve.real(20,seed = seed)
df = df[,-which(names(df) %in% c("t"))]
cluvid.raw.result = data.frame(matrix(ncol = 4, nrow = 1))
colnames(cluvid.raw.result) <- c("method","index","n.group","accuracy")
cluvid.raw.result$method = c("kmeans on raw")
cluvid.raw.result$index = c("average silhouette width")
cluvid.raw.result$n.group = 20
set.seed(seed)
df.clu <- kmeans(t(as.matrix(df)),4)
cluvid.raw.clu = df.clu$cluster
cluvid.raw.result$accuracy = get.clu.accuracy.raw(cluvid.raw.clu, 20)
simuvid.realraw.20 = rbind(simuvid.realraw.20,cluvid.raw.result)
seed = seed+10000*i
}
simuvid.realraw.20 = na.omit(simuvid.realraw.20)
write.csv(simuvid.realraw.20, file = "simuvid_realraw_20.csv", row.names = FALSE)
### Summary the validation result
simuvid.realraw = read.csv("simuvid_realraw_20.csv",header = TRUE)
simuvid.realraw.summary = simuvid.realraw%>%
group_by(n.group, method,index)%>%
summarize(mean.accuracy = mean(accuracy),
sd.accuracy = sd(accuracy),
se.accuracy = sd(accuracy)/sqrt(200))
simuvid.realraw.summary